Part 1 Assignment 1 (Part 1) Question 1 In the lecture we have implemented two ML models, one using PyTorch and one using Tensorflow for predicting tomorrow's price the stock mtr (0066.HK). This question asks you to implement yet another ML model using sklearn's Linear Regression method using the same set of stock price (i.e., 0066.HK between "2010- 01-01" and "2020-06-30"). You should submit a Jupyter notebook that includes the three ML models (i.e., the Pytorch and Tensorflow implementations from the lectures and your implementation using sklearn), and compare their accuracy on predicting the price of 0066.HK during the period "2021-01-01" and "2021-04-30". Question 2 Choose a suitable method (except neural network) from sklearn to train a Machine Learning model using the MNIST data set given in Lecture 3 for hand- written digit classification. Provide a brief explanation of your chosen method and why it is suitable for this task. Question 3 and 4 will be released later. Part 2 deadline will be announced later. Submit 2 ipynb files to Assignment 1 Part 1 (Jupyter Notebook format and 1 Jupyter notebook for each question; programs in code cell and answers to the questions written in the text cell). Please note that the ipynb files should already be run on Colab with clear output data/graph. All the submissions via Moodle.

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